The following sentiments and entities have been modeled for this demo. Only these items will be detected. Additionally, while many common expressions of sentiment have been added to this model, it is not currently exhaustive and serves only as a basic demonstration of AKIN's abilities:

When a sentiment-based compound concept is detected, you can expand the node and see the "Detected Parts", which represent the parts of the concept that were detected, as well as any entities assigned to the sentiment, as shown in this example screenshot:

Please input text below and test AKIN. A starting example of input text has been provided for you in the input box below. You can edit or replace it.
The results are sorted by high relevance items first and then by word position.

First please confirm that you are not a robot to start.

Results:

Double-click on result nodes to expand and explore their properties.

The first call to the service may take a moment to bring the service up. After that, all subsequent queries to the service should be fast, unless there is a long pause in between queries.

If you are currently using any of the big name Sentiment Analysis Software solutions, the metrics they show you are quite likely innaccurate. This is because their software is unable to precisely disambiguate and assign specific sentiment to the correct entities. Results are more fuzzy, generic, and many times mostly academic in nature, requiring manual disambiguation and noise reduction. AKIN NLP provides output that is immediately useful for your workflow automation streams by being significantly more targeted, specific, precise, and accurate using its built-in intelligence.

It's easy to build basic software that can detect simple phrases that denote sentiment, e.g. "I like this!", "That's great!", "I'm not happy", etc.
It's also pretty easy to assign points or weight to the detected sentiment and come up with some overall sentiment score meter.

However, it is much more difficult to correctly and consistently disambiguate positive sentiment from negative sentiment, to understand negation, affirmation, direction, exclusion, inclusion, and comparative and contrasting statements, and to accurately assign sentiments to specific entities such as in the following examples:

"Do I think that Space Cheese is awesome? Well I really don't."

"Do I think that Space Cheese is awesome? Sure, well, not really."

"Space Cheese is not a great product, but you know what? AKIN is a different story."

"I really like AKIN but Space Cheese not so much."

"AKIN is awesome as opposed to Space Cheese."

"Space Cheese is horrible compared to AKIN."

"AKIN is a premium product. That applies to Cognexus as well."

We strongly encourage you to test how well other software solutions handle the above examples. Most of them will not be able to detect the correct sentiments and assign them with accuracy and precision. They will do even worse in the presence of text sources that generate
highly variable expressions and unexpected grammar. Their results will often be generic, fuzzy, and more academic than practical. AKIN is one of the most accurate NLP systems available today and leads the next-generation of these systems.

AKIN supports complex entity classification and the ability to specify the detection of class level compound concepts. What this means is that AKIN can also detect general concepts like:

Positive Sentiment Product Feature from a phrase like, "The UI in AKIN is especially good"

Additionally, AKIN has the ability to deal with a very high level of textual variation such as mispellings, typos,
grammatical errors, or unexpected composition. Please test this for yourself below. You can edit the text in the input box below, hit the Analyze button and see what kind of results you get.

The demo above provides an example of the AKIN NLP API’s ability to sense and understand the conceptual meaning of free unstructured text
, and output that understanding into structured standardized representations. It's high sensitivity leads to greater adaptability to variation
than other technologies. Additionally, AKIN provides traceability information for downstream consuming systems and users, delivering unparalleled transparency.
The traceability info shown in this demo is only a small sample of the full set of information that AKIN provides to consuming systems.

Here are some more examples that illustrate some of the very difficult scenarios AKIN is able to handle:

Example 1

With regard to the ui in space cheese, I have some mixed feelings about it, and please don’t take this the wrong way, but
it was pretty difficult to use, and many times I found it frustrating. It was just bad really.

Example 2

I'm disinclined to like space cheese. On the other hand, akin is great.

Example 3

I've only had a chance to play with these products a little bit, but so far I'm liking what I see in AKIN.
Space Cheese on the other hand, not so much.

Example 4 - Note spelling errors and typos

In terms of its UI and API AKIN blows Spaccheese and MarPizza out of the water.

Example 5 - Notice how AKIN understands that the term "blows" communicates negative sentiment here instead of the positive sentiment it communicates in example 4 above.

Mars Pizza blows. So does Milky Wayshake.

High Level Features:

Built-In Artificial Intelligence

We've removed the need to do the expensive and time consuming work of feature engineering and extraction, testing and selecting machine learning algorithms and approaches, tagging tens or hundreds of thousands of records, and training ML Algorithms. Our AI Model allows you to simply configure/add the concepts you want to detect and AKIN can use that information in combination with its built-in intelligence to detect those concepts and make inferences and determinations with a high degree of accuracy.

Direct Injection of Knowledge

Sometimes you know exactly how you want the system to interpret some information or context, and you don't want it making probabilistic assessments. AKIN also supports explicit knowledge and rules.

Easy to configure and manage Domain Knowledge Models

Directly via API and Model Manager User Interface

Extremely tolerant of textual variations

Varied expressions, mispellings, and grammatical errors

Highly Transparent Model

Easy to find out why something was or was not detected, and make necessary adjustments or improvements

Complex Entities with properties having multi-level hierarchies and multiple relationships

Graph + Hierarchical Relationships

Advanced Inferred Concept Detection

Some concepts are not explicitly stated but inferred based on the presence of other concepts and ideas

Command/Intent detection and user interaction

Detects concepts in large paragraphs or documents of text, as well as shorter user commands or intents equally well

Advanced Noise Reduction

Highly Optimized Performance

In-memory distributed processing

Sub-second response times

Deploy Anywhere

API dll can be hosted on any .Net compatible platform in the cloud or on premises

Multi-lingual support

Although currently only certified for English, AKIN has been designed to work extremely well across cultural domains, including Asian languages.

Configuration:

To configure AKIN, you feed it knowledge in the form of standardized values of concepts and ideas you want detected and fed to your downstream consuming systems. This is done either through easy to use API functions/methods, or via the Model Manager UI. Additionally, you give it synonymous and related terms for the concepts you define. AKIN uses this knowledge
to make smart determinations about the text you want it to analyze. You don't have to spend hours tagging up
thousands of records to "train" it in a haphazard, disorganized way. You directly feed it knowledge in an orderly fashion, and you can always see what it knows and what it doesn't know. Then, AKIN uses its sophisticated probabilistic AI to detect concepts and ideas within the text even when there are a lot of variations
in the way something is written, such as spelling and grammatical errors.

Why do I Need Natural Language Processing?

For many types of businesses a large amount of information is still collected, bound up, and stored in unstructured or semi-structured text. Every year, businesses spend enormous amounts of money attempting to successfully manage and
extract value from this data, that frequently require manual efforts enhanced by discovery technology that often feels rudimentary and inadequate.

Additionally, businesses and application developers are looking to create more natural interactions with their information systems for consumers, customers, and their employees. They want the ability for users to be able to
write or speak inquiries and directives naturally, and have their information systems understand and be able to process this information. This is no easy task.

The problem is that everyone communicates differently. Individuals express their thoughts, ideas, and concepts in so many different, non-standard, unique, and individualistic ways.
Exacerbating the problem, people also make mistakes, or speak different native languages leading to unexpected grammatical structures when they write or speak (speech to text). For example, in a medical domain
people may have symptoms or issues they express in different unique ways. Several people might describe the issue of having difficulty breathing in a number of ways:

“I have a hard time breathing”

“Sometimes it’s painful when I inhale”

“I find myself gasping for air”

“There are times when I can’t catch my breath”

“It hurts to breathe”

Etc.

Downstream consuming software systems need to be able to understand those concepts and do things like alert key stakeholders or gather significant data for analysis and research.
However, those downstream systems need a standardized representation of these concepts, in this case the symptom we describe above, something like “Difficulty Breathing” or “Labored Respiration”.
Having a single representation of this symptom makes it extremely easy for other systems to use this information in a structured way.

AKIN Natural Language Processing provides unparalleled accuracy and performance allowing your business to effectively extract the value from your unstructured text and natural language queries.
There is no need to rely solely on centralized Cloud-Only-Based solutions like Google, Alexa, or Cortana that tie you down to an ecosystem and take your data out of your hands. With AKIN you can host anywhere on-premises, and use any speech to text technology with it, like Dragon Naturally Speaking,
and those already freely available on mobile devices.

AKIN has been designed to be super intelligent and high performance, and yet still be very lightweight and efficient. It can even be hosted directly on mobile devices or very lightweight client Virtual Machines.

For more information and licensing inquiries, please contact info@grappledata.com
using a valid company/organization email address.